{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"auto_encode_vae_cifar.ipynb","provenance":[{"file_id":"1Q_l4g0MFmxDj6j56aWqY7BvRRGqixEVN","timestamp":1566773412870},{"file_id":"1MrBSRG2cjs7qiG1o-eCDBTelLUE7x9IN","timestamp":1563249793943},{"file_id":"1CCJ3Q4pMZwAbwVChO1nNXiwjX8Q743-9","timestamp":1563160975476},{"file_id":"173_vpr3ItdJNyybO-Sbfh44EUaAjl_Iu","timestamp":1563141001569}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"qQdrqnmeqc8M","colab_type":"code","outputId":"22b603b7-88cf-4cca-c00c-9f80d3062338","executionInfo":{"status":"error","timestamp":1567707747701,"user_tz":240,"elapsed":173,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":130}},"source":["https://github.com/darleybarreto/vae-pytorch"],"execution_count":0,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-b3c100c5a486>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    https://github.com/darleybarreto/vae-pytorch\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","metadata":{"id":"uSbUFEsttU_F","colab_type":"code","colab":{}},"source":["import torch\n","import torchvision as tv\n","from torch import nn\n","from torch import optim\n","import torch.nn.functional as F\n","from torch.autograd import Variable\n","from torch.utils.data import DataLoader\n","from torchvision import transforms\n","from torchvision.utils import save_image\n","from torchvision.datasets import MNIST\n","import os\n","\n","if not os.path.exists('./vae_img'):\n","    os.mkdir('./vae_img')\n","\n","\n","def to_img(x):\n","    x = x.clamp(0, 1)\n","    x = x.view(x.size(0), 1, 28, 28)\n","    return x\n","\n","def to_img_3(x, h, w):\n","#     x = 0.5 * (x + 1)\n","#     x = x.clamp(0, 1)\n","    x = x.view(x.size(0), 3, h, w)\n","    return x\n","  \n","  \n","num_epochs = 50\n","batch_size = 128\n","learning_rate = 1e-3\n","\"\"\n","img_transform = transforms.Compose([\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n","])\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DynMaOevuKA5","colab_type":"code","outputId":"285265dc-d444-47ef-c86d-277d564a56bb","executionInfo":{"status":"ok","timestamp":1567706706760,"user_tz":240,"elapsed":132301,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5OzTqE9DuUBT","colab_type":"code","outputId":"4b303567-7352-4288-af71-af4d8cee5e80","executionInfo":{"status":"ok","timestamp":1567706713079,"user_tz":240,"elapsed":62240,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# Loading and Transforming data\n","transform = transforms.Compose([transforms.ToTensor(),  transforms.Normalize((0.4914, 0.4822, 0.4466), (0.247,            0.243, 0.261))])\n","trainTransform  = tv.transforms.Compose([tv.transforms.ToTensor(), tv.transforms.Normalize((0.4914, 0.4822, 0.4466), (0.247, 0.243, 0.261))])\n","trainset = tv.datasets.CIFAR10(root='./data',  train=True,download=True, transform=transform)\n","dataloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=False, num_workers=4)\n","testset = tv.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n","classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n","testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\r0it [00:00, ?it/s]"],"name":"stderr"},{"output_type":"stream","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"],"name":"stdout"},{"output_type":"stream","text":["170500096it [00:02, 60069866.24it/s]                               \n"],"name":"stderr"},{"output_type":"stream","text":["Files already downloaded and verified\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"kk1JhJG1hqCA","colab_type":"code","outputId":"a0a1bfeb-aa7f-4d42-986a-2f4ce5ecc09c","executionInfo":{"status":"ok","timestamp":1567706736090,"user_tz":240,"elapsed":1093,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# ls drive/My\\ Drive/MNIST\n","ls data"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mcifar-10-batches-py\u001b[0m/  cifar-10-python.tar.gz\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9WuxWeEGyb98","colab_type":"code","colab":{}},"source":["# cp MNIST drive/My\\ Drive/MNIST -rf\n","# ls drive/My\\ Drive/MNIST\n","# ls "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BlwxYbAJtmTm","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LVW8Wx4qwkuH","colab_type":"code","outputId":"61aa7933-0004-43ec-d1fa-cff85b4635c2","executionInfo":{"status":"ok","timestamp":1567706737413,"user_tz":240,"elapsed":503,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["dd = next(iter(dataloader))\n","img, _  = dd\n","print(img.shape)\n","\n","\n","c = img.view([img.shape[0], -1])\n","c.shape\n","# a = torch.arange(1, 17)\n","# b = a.reshape([4,4])\n","# b.shape\n","# b.view()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["torch.Size([32, 3, 32, 32])\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["torch.Size([32, 3072])"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"eZEhMHnD0Zwj","colab_type":"code","outputId":"a422f982-6849-4d4d-e13b-5eafe87802e1","executionInfo":{"status":"ok","timestamp":1567697784369,"user_tz":240,"elapsed":1569,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":255}},"source":["dataloader.__dict__"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'_DataLoader__initialized': True,\n"," 'batch_sampler': <torch.utils.data.sampler.BatchSampler at 0x7f5e18a9ec88>,\n"," 'batch_size': 32,\n"," 'collate_fn': <function torch.utils.data._utils.collate.default_collate>,\n"," 'dataset': Dataset CIFAR10\n","     Number of datapoints: 50000\n","     Root location: ./data\n","     Split: Train,\n"," 'drop_last': False,\n"," 'num_workers': 4,\n"," 'pin_memory': False,\n"," 'sampler': <torch.utils.data.sampler.SequentialSampler at 0x7f5e18a9eb38>,\n"," 'timeout': 0,\n"," 'worker_init_fn': None}"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"PRn1GeFTEfIP","colab_type":"code","outputId":"6a0b03d5-6ab8-4d23-ce3d-96577616ef9c","executionInfo":{"status":"ok","timestamp":1567706765887,"user_tz":240,"elapsed":794,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdata\u001b[0m/  \u001b[01;34mdc_img_cifar\u001b[0m/  \u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/  \u001b[01;34mvae_img\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jlvyzuS_wscm","colab_type":"code","colab":{}},"source":["# for data in dataloader:\n","#   img, _ = data\n","#   print(img.shape)\n","\n","import os\n","!mkdir dc_img_cifar"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4aNppKkMtzEf","colab_type":"code","outputId":"4646dbcf-6a86-40e9-b511-d20b93ec1c2d","executionInfo":{"status":"ok","timestamp":1567706816479,"user_tz":240,"elapsed":42699,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":425}},"source":["\n","# Writing our model\n","class autoencoder(nn.Module):\n","    def __init__(self):\n","        super(autoencoder,self).__init__()\n","        \n","#         self.encoder = nn.Sequential(\n","#             nn.Conv2d(3, 28, kernel_size=3),\n","#             nn.ReLU(True),\n","#             nn.Conv2d(28,16,kernel_size=3),\n","#             nn.ReLU(True),\n","#             nn.Conv2d(16,1,kernel_size=5),\n","#             nn.ReLU(True))\n","        \n","#         self.decoder = nn.Sequential(             \n","#             nn.ConvTranspose2d(1,16,kernel_size=3),\n","#             nn.ReLU(True),            \n","#             nn.ConvTranspose2d(16,16,kernel_size=3),\n","#             nn.ReLU(True),\n","#             nn.ConvTranspose2d(16,3,kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.Sigmoid())\n","\n","#         self.encoder = nn.Sequential(\n","#             nn.Conv2d(3, 6, kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.Conv2d(6,16,kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.Conv2d(16,16, kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.Conv2d(16,1, stride = 2, kernel_size=5),\n","#             nn.ReLU(True)        \n","#         )\n","        \n","#         self.decoder = nn.Sequential(             \n","#             nn.ConvTranspose2d(1,16,kernel_size=5),\n","#             nn.ReLU(True),            \n","#             nn.ConvTranspose2d(16,16,kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.ConvTranspose2d(16,3,kernel_size=5),\n","#             nn.ReLU(True),\n","#             nn.ConvTranspose2d(3,3,kernel_size=5),\n","#             nn.ReLU(True),            \n","#             nn.ConvTranspose2d(3,3,kernel_size=5),\n","#             nn.ConvTranspose2d(3,3,kernel_size=5),\n","#             nn.ReLU(True),            \n","#             nn.Sigmoid())        \n","\n","        self.encoder = nn.Sequential(\n","            nn.Conv2d(3, 6, kernel_size=5),\n","            nn.ReLU(True),\n","            nn.Conv2d(6,16,kernel_size=5),\n","            nn.ReLU(True))\n","        self.decoder = nn.Sequential(             \n","            nn.ConvTranspose2d(16,6,kernel_size=5),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(6,3,kernel_size=5),\n","            nn.ReLU(True),\n","            nn.Sigmoid())\n","    def forward(self,x):\n","        z = self.encoder(x)\n","        x = self.decoder(z)\n","        return x, z\n","      \n","      \n","#defining some params\n","# num_epochs = 5 #you can go for more epochs, I am using a mac\n","# batch_size = 128      \n","\n","num_epochs = 2\n","  \n","      \n","model = autoencoder().cuda()\n","from torchsummary import summary\n","summary(model, (3, 32, 32))    \n","\n","criterion = nn.MSELoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,\n","                             weight_decay=1e-5)\n","\n","for epoch in range(num_epochs):\n","    for data in dataloader:\n","        img, _ = data\n","        \n","#         import pdb; pdb.set_trace()\n","#         img = img.view(img.size(0), -1)\n","        img = Variable(img).cuda()\n","        # ===================forward=====================\n","        output, _ = model(img)\n","        loss = criterion(output, img)\n","        # ===================backward====================\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","    # ===================log========================\n","    print('epoch [{}/{}], loss:{:.4f}'\n","          .format(epoch+1, num_epochs, loss.data))\n","    if epoch % 10 == 0:\n","        pic = to_img_3(output.cpu().data, 32, 32)\n","        save_image(pic, './dc_img_cifar/image_{}.png'.format(epoch))\n","        pic_orig = to_img_3(img.cpu().data, 32, 32)\n","        save_image(pic_orig, './dc_img_cifar/image_orig_{}.png'.format(epoch))\n","        \n","\n","# torch.save(model.state_dict(), './conv_autoencoder_cifar.pth')\n","# torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/conv_autoencoder_cifar.pth')\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1            [-1, 6, 28, 28]             456\n","              ReLU-2            [-1, 6, 28, 28]               0\n","            Conv2d-3           [-1, 16, 24, 24]           2,416\n","              ReLU-4           [-1, 16, 24, 24]               0\n","   ConvTranspose2d-5            [-1, 6, 28, 28]           2,406\n","              ReLU-6            [-1, 6, 28, 28]               0\n","   ConvTranspose2d-7            [-1, 3, 32, 32]             453\n","              ReLU-8            [-1, 3, 32, 32]               0\n","           Sigmoid-9            [-1, 3, 32, 32]               0\n","================================================================\n","Total params: 5,731\n","Trainable params: 5,731\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.01\n","Forward/backward pass size (MB): 0.35\n","Params size (MB): 0.02\n","Estimated Total Size (MB): 0.39\n","----------------------------------------------------------------\n","epoch [1/2], loss:0.7772\n","epoch [2/2], loss:0.7747\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BRW1b2njt0WS","colab_type":"code","colab":{}},"source":["# # torch.__version__\n","# from torchsummary import summary\n","# summary(model, (1, 28, 28))\n","# data[0].shape"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rrKKjecP047m","colab_type":"code","outputId":"91e12eb6-bfd7-425f-8bb8-4649a8513bc4","executionInfo":{"status":"ok","timestamp":1567696996492,"user_tz":240,"elapsed":1202,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["loss.data"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(0.7757, device='cuda:0')"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"4Qweo7mr3WgD","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qhl12L3h3YF8","colab_type":"code","outputId":"1ca79e3d-3baa-4b41-a62a-fa78a16c18ad","executionInfo":{"status":"ok","timestamp":1567697370570,"user_tz":240,"elapsed":3339,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mdata\u001b[0m/  \u001b[01;34mdc_img_cifar\u001b[0m/  \u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/  \u001b[01;34mvae_img\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LGwa_cef6LP8","colab_type":"code","colab":{}},"source":["mkdir dc_img_cifar/"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3XDM-dEk6cHR","colab_type":"code","outputId":"47cc6e89-76cc-4edd-b4b6-5a183fad9e82","executionInfo":{"status":"ok","timestamp":1567697015700,"user_tz":240,"elapsed":1128,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":153}},"source":["# from matplotlib import pyplot as plt\n","help(os.path.join)\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Help on function join in module posixpath:\n","\n","join(a, *p)\n","    Join two or more pathname components, inserting '/' as needed.\n","    If any component is an absolute path, all previous path components\n","    will be discarded.  An empty last part will result in a path that\n","    ends with a separator.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"53x80mMf6ldn","colab_type":"code","outputId":"2bd1eeb8-1248-456d-a5a9-1db02e36ea90","executionInfo":{"status":"ok","timestamp":1567706826011,"user_tz":240,"elapsed":759,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":281}},"source":["from matplotlib import pyplot as plt\n","files = os.listdir('./dc_img_cifar/')\n","files.sort()\n","for f in files:\n","  plt.figure()\n","  a = plt.imread(os.path.join('./dc_img_cifar/', f))\n","  plt.imshow(a)\n","  plt.title(f)\n","  \n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAACECAYAAACAhtD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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/yuHym1eMyn3964q/m+0H0cTFn1SvHk3gRznWxaigRQC+bn59EzlwPm0nbTvcOWwpCTq4\nGa38UFVy6Et/cw8zPzM8gg65suyMglQGUqKoU0PLUh4Q9B0AqWQPig6gEwnNJCS+2M5OiFmnzSZx\nmuXUXNrbK8tZeT3AzwCuQsd5suEOtKVJfPbx5GeBnDprLE44xpoKRC5bAzZ9SA8l3cYAL8NRIWnJ\nAegMSt8qcG57QCJhg1FtHdHVtoKMDTyeqi4QxC/ehTalsiKbnDylDFZs+lWoNoaLrTHquy4chmha\n0VuhlVg+gnjoAQIECLCDIODQdwCodArXGcDtXUh3ciOt43bTJ6QWEONF6mVCSiQSdD3XTnotNJmA\njyu+C6VSSrdfBVoLqI03T5l/ESKDl6k9LVEaGqI0dVcSqXLLIIYXN9OKSKoRMJyJjmuxCrCCnsPR\nss+z8QIaBcjF8A37zgNuGJG2FEMyqaM8JlWmaq/bdEYRNppoPz/tv4Xkbe2+69v6g2fHHYiHIZ55\nT4hc6tBNHl357PaGns1dNE+cSrh5Ei1uGrJhABRuppN07z+I1VlPRn2uYf+L8XezqW0rmZfuJBSe\nA8YmV6WWseDKwVnjXwJeuqywM89zf7ifKbNnMuCuK3h+a6DY1FIufO5Us21phNj+MPYRLzxxHToO\nzVJ0cGS7bC4U5Hi2KTdSU5zravLhbOXUb+AjXsolkyf3dTMuodBw2jj6cv01a7WBQ0/G1R2iilzR\nbiaFG9LjKY1HnO0+FM8i3AF0pmGcC/3mey5JuoQ2ZmhxNrF4YGjjZzsj6JWFRX2voS/TQ0OmF+VG\ngAFCIUteBnDUaqKR1XgGgmPRn93BL3ETmUYoAv7uJ5HpJBJtoFIot5vXXv0FAHfdXWpCDeG0ttKa\n2ToJOSqForwjk/XVfKsTpj+iwyYszztn0UxxjLQVjFL52Si3HrK9RZxBaXZc5eIgKF+fqqbFB382\nxr9uHnYTS2LJSv0lN3esqIqYHwhEw9nIATn2X1ZGXgohF1xXkRFFvaNL9yTTzKqHxngdqqOrzB0K\nYzsj6AEKQZL94KbAiYCT9nUmgdBYCB0CYr08/aTMEnZ85wrEg5EwEmpmz73PA2DX3XtZ8tbfue23\nz2WLWGv1CdOnM7UuxMbNw0/pta3gdTQBL5UEsKPEuRGHsot0fwLyrQ1Fvh5cVAZRPlJXZVOPnVLD\nG0fNZfOjLw6/eUXQsUbHod+0obpQVMvqovT19xKv0cas4kjW6xRKP6oeZQrExVVpMqb0mM4uaupj\nRDNJenurzX+ksfWn+AABAgQIMCIIOPQdACvWbiAUhmikGcftomGCmd1Fc9uO00JuoisHBGSQgZU1\npvJHlbBORZI9GgpFmDx9DmeeEWXF2m4Aampa2bB+PWOaGuhPd+Omti3nq0oRCkOdQCjlycUbw+Ck\ndXQduyoPoZVY3eg3a5lT/9u+I7MhAAAgAElEQVS0g8tKlkck0i3Q3a3XA/X1jYhEEVGIs3WGsqtc\nRLlk3CTdXblW+Z2dG2loaMQxehuFIhwpHgcoHyGV4fQ99ubB1UtZ+NboWPyvWa9Fkf2lkpMVQEN8\nCqtXrmbsBD2u4vEa7W0qUlApmv/d+weSpPsydLop6iP6HgO9mxlQabo7u+leN7Q1nyg1El2swsoq\niIceIECAAAEG4Vml1AHlCgUilwABAgTYQbBV1mnX3aLd0FMqRl/PCtjQgRdr26+c8Ct9/DmyHd/5\nEIOVQ7ZM/oKgA704ngDj9wQgXNNFS3MNTn+aHqXXXV894zQA/vjg39h5yljqa7XGuaXWobZpH4R0\ntjqROkRig9bTVrOvClnjKn0UszpSKkM61U+yt4PNa7Tqbe3qNWxY283GlLB0+TL6MuYeGQU1US69\n8OLs7f7vnvtxI02s7F0JSzqhbm/zKt9Fp0IbC7XGCM9VPt2n6LirAGEHYiFtBFtjukU8RCgeIhpx\nCIccrHe04wgbl3XCQA8ktWCiYXotPesX0toUJpZSpMI6w9B//9vxAPzpmcUk63W+n2URl47OENOm\n17E0BdTqNuwSgz0iuVFjIuYvzOBs6PnWBAr9dV20mOMJI+tY3w81faAcxbq39TeeXu9Sl0qTTK7n\nrEO9MMKvvfsm08c04YSWEpGF+tXETkCrRJeCmHjn7E1u3yvkB2itUdJoRfQAWkhjfFDVv3SRPlAm\nR3T6eehYBEv6YFktJM0Drq6P8OVzLwHgd8/pMfKS06hv+6QDMyOe1nYyOrvgnr5m2f5pX5B/CEXN\nCzbSkNkh+BA6jXn+CCRvvzut6OgZYMXKLl7fsJLPHull8lm6uZ+aeJROY8XhusK0iNBnztsshmEK\ni6IKKRbL2bb3AQPmZmGlH3dxZ4oW06UzKUXXQC/7TdI+w88v0ok9dp40lljUISRCeAsmu/A/d28q\nQ8Z16U66dPZqi745E1sLX1gEW4WgW5li39IV6Eeqo3CMbX9ORv/w9fdSK+P1W3w66EeLojMNgI6+\nYbvnJujVUb/TEmZdXxJZk0Ri8Zza65tqGFPfSJ3SbasJhXFEkMx6cF/ThcI7Q2gmKBMU25iUicqg\n0r2Q6gHRr1kcB5XuBrcXkahOYQbQvwlZ9g7JhWvoWaEdHZZ3wcsKMtn34vv0fbn2VZG6OrrWDcDK\nJshMAMdOjqvRbzsKvfb6Qo7J9l2GdexuawSTUWSUoi/jmDnSvF9HIBqBCc3QqQlkV5MDmRaStWnq\n1w6Qb8jXMK6ZTUn9HtxOh3BzmKU1AmsUmAG/MCoszPd49n3qGN7XjJonSeH1p34g7Rf/Z7zCXRml\nC9bqb7y0McPsDSEi0dxUuBPGRKiJg+P2Q9i8e4mbml7GSwaxGm3X45BrABlFt9Qf5qsHPbGmzXaV\n92whoN4kQAIiE2HcO9D0R2ju9axnOvu8OuLjTL7WhQ68EoJaR8c02GgKbETn2VhvqsM0ca15SX7E\n8Yj5SfrQ2zPh7ZBOHvxBU8Q2l7z9urCwpjbCsn6Hvu4W/GiujRARiBpmJBQWlGieZEC8b1kpMS91\n3CKGZ0rYI3rUT2wIEza1OLj46cq4Vr1fHw0h4sVH31Lw11YTDpFBCOGQSdcWvaYUApFLgAABAuwg\nqCZJdAidsGWFUuojIjITuB0dHPxZ4CylVEWm+X2ddk62S9EUUMiQvkrVs9da9KOVcB3pNhx2dw2s\nmY6iF5WXDG3TwoW82/EmNS/qNMY13dAwCXpWav4MYBovM3YKhMZD3/OewGg58DiDUncOAeUddAb6\no6SiCmkClWmBpOWpkugl/nQ8+wy/KMC/6nGAGuiOQMR6S4h+lWk3d60bEohkoMuFpd1eQ1SGzak6\nZCBNTTjXKDlJjFSj5nhbo0LruBCLIsAY8RZnhcyqfb8HqMDb0i/6snKbfuAtNLu23gZjirAE2Dlv\nYVib6USkF3jI5+4Xx8urZPEyIwr7nDH9F0lrXt+G0PL3ow0h0+gZjg6dOBvd3W1q2UY0+5sh1/ym\nF1hj6phujtfiSShNG3YROM7UPTjGZi4cYGGX4vV7V4IsyzkXESEckuzCLmQWejHRzRoN8UAI/XiY\n+wtQ7wCubkQq4lDvs83o3KSXMBMb6nGQLW7WnxO0SyDjCgMoEz21elTzTs9H+1jYqO7XAN9RSt0u\nIj8EPkuR+P75UFkaVQnBtsu4CNrlv9iQdvCSp4XRo7gQMXTIHR4uhR22oWPdWt58YyC3xkIp6lfg\nBfnYCuhTcTZvWgFr+6ElDvUm+OjG2dAch5ZxsMRMVir7z6gZzLuoc2CnCEwLmxGAfo12VPglNQ46\nzmd/huzytSkKTj+800XHu/10jM+dypKhMK8a4aaTFibZ1KetZAlvTPTXjQDvM9fNMNu3AM+NSSOE\nNiUcb35vwqS1zrcPjAEzRD9Loxnuy1xSy+HNSbkOHKHwu4j7vO4+9h5N+cR8FBECpoHsA2Ne8sRJ\nfuFjKma+Twrd5WvQzzy7gvtPz/15gbnFUOlYCnjq+x3A7wadCzuitQymuf46RlPW6+Rt60RIm8pd\nV7M4Fg/eq6OuJ4+fzfh0Dytu6+ftI3RY3NOPbRpR+l7Ivzdf8xcGUv0ZNvcMjRWs6L2KyFTgw8AV\nwAUmcfQH0LmFQccnupQKCTrd1cQpqNT+1KX0BBFHj9L8F1Wc53Ni7ojF3xhNbOrthBUuMAXWNuGx\nZWHoiOi/QdoxtILU7neH4NUIRB1IWVk5mroKeuT6Od+NjpbRLjKc/6woTKiB5hQMtEJt7mJtuSPw\nqglkND7Eiol4+acNG2rfdQp43uyvwQtjmw8rvX7X/M6x3BVyR/ZYc6zRPNuYEOxWQ+3m3P4Qjj2v\ny9VQST6PkYegueZ/g/puiBpF51HjvCJLLQv6L4G9fNf6HUjL4AKzbRhOW4Gr+sEGbcuHlWVvbV9W\nB92tAcI1Qr0vRMHmniUA3PPbjYydskwHyHjsYQAWPGZLzQROpeYSPa1+fYizUTKpp5J4tL7geVcp\nkpk0vZkkNZHR5dC/i46Ib79/K9ChVDYaz3JgSqELReRzwOdyjw4tTkF1iKEtESw5KObosguwsOCZ\nJStHIh7Jx4A5evcj4axxxESBD5tO1gJcmwauGACeNtc9WHkVa9NkbRKkBpSVI7SiqYO1EykE07mn\niw5QMUk8gpDxTmfDw4EeIQMCGxwYMKuBWtccD8OaXqjPfXcpJTDNPHA9nj7R16wPouOlHGRaDpre\n96KX/g14IgjbRH9c6T503JQnMMS9kBbPXhgFXKE3L/6I2H8V+7+cCuzmu7HkbS3S6GXczyq7bRjk\nIxAxGaWbfbrbQ41I7J9Hop8j/9nK4CQ83ajlztcBP/DZHLQVeAI/slGVbto2494Xg5CbxCTdqeMS\nve/gEzjhA2dx5VVXFrhqMXAtfSYlXjtw2Bfmc/wEj9dOU5qY9v6gnW8ZPnbeyRDatw3IsOz7V/DT\njYWumFjoYFmUVYqKyEeAtUqpIa07lVI/VkodUIlRfIAAAQIEGDoq4dAPB/5dRE5C81WN6GQ2zSIS\nNlz6VKqSIm+JWX0ArcPNx+c5b95Exvi4mbc7k/z6Z6/Apj8Os87zyZrrnYLOsRwxW9CCUMP2rMbj\ncqYCiTD0JGK8xhEAvMsRvPo2cFt7+WrjkDUuVtbEDlO51TTmm3yafTuljxV9nwxkDYU70KyH3yjc\n1teFZodXmJNrM3r5v9oFNw0Zv6QSwnUONJm6ozDV0czlB/A8EKLAIXmttTbl+WHE8p/EXn8A2txu\nHfBrc7zgetAmcyw2AioO5v3bEuf+x2xbTGVTgXOAwSGJC6IR5AN6t9aXk2038x2iUd2PTkI/hhU9\nzUR/ok7gO3m3PAq99rPirU+Z8j/oB64xa52THWRfSiLLoXcWX21nyPUG2dqiFygYeg6A3sl9VSWK\nfuKHl+Xl6tLYDzgGuK7EtZf/Hvj9gjI1rC5zvjDKEnSl1EXARQAicjTwVaXUp0XkTuA0tKXL2QxO\nLD9MTKawBnKIOHkeiX0Lf7BZjVE+eMZe3Pf7/KCo1eAbMCfmfYeS+ZldYDO3W4VvAuYBD+OZDZ8K\nHDUbbtqlDRaW+fj9GbKmDTGBAb9AIl8EkNedWz0iSy96FNrouCvQIz+OJuZW3hFHS7BeVmTVdqkk\nLBfYlNbHXF8aPKAlBP3mUDIEJ5vbO94diOINfnusAz09NZsm+M3M7VRlp69VaKXoauCNQS/JdyG+\ni0J578M63RSjPvb66ykflr/NyEty7vW+QiULwwHR/m+EfV13vLnfVHJVBXPyLm/ESzB3J/DaUnj0\nFtAPqXUcv8YmP3mRbKf9/Xza93W4EP3O7f39j3FTVg12e9Hmb0uxIHNQoFGvv/UQG8fNKHLBDGBJ\nRbd+Hk//szUwHGXzhcDtInI5+hlGOHLxCBDzLydoMzY5WdMgYKPS1nL+401RhbQOVUW0MxDTNkAl\n4aJ5Gzt/f81sawkBRwN3mCMO2npjr08Lr/z4IlhVOKEEAI0tQB30NkLS73fn50cKCZTxFkvL0er/\nBjxi14mmrBlys0F0Aa/Z57HsvAsS8upuylX8zIwIzYYyjBU9N/yNXD+Xs9Fc5nhyFwMtkCU7trlJ\n31O+arZ/ogLYR7evqS7vfCkqlAF8SbVXUzjfKOiVwkkLfFX+BzANcs0fZ1CSUAhZmzJ/k+J5RSrB\nawC3VLDaA+AyaIdrOBg4UQvUTWUfR08O/MCW7S5wvcZoOrkMNRtSsevOPPp0WltbuPiCz3HldT/O\nOXf+aR/j+rtK8dzbDqoi6EqpR4BHzP4itP5q20MkQeLi3EPtfwGe9ndovYb9xvxz6e7O8Njji1Av\nPkr1aAbOqrCsg6aYp6KX6tobzJoK1QLWkrcTLak5BWj8zyhPXHaEOZNVvXsIORANQSakOc4eP09V\nYJEZQVNIf4aWLvTYnIBHTdPmvOUOHd/1WYh3sNWBzWG9QsjLVCN4xNuaF+Y7Lf7cbCegRTGg6ZnV\n+4mvKTW+63b3NW+p2V9GcTW4v1Et+Ry6NfzJH/X9wDV6FQVe5qJieAZDRNGSqCN+aiwKzkLP/0BF\nXF/2NXqeg34vgtw0JSXQXkX2hiz+pf8WGD5/Pox14BPAHZbI934Vrv07hizkoMeFzeKtlqLmne5H\njvQR0M+0GLjV/J6AHiVj8JS2oMVFizATu49v+V88yaYfaeAvwPPm8eMuHJrxW5DoniROLSJCpGHS\noHtsL8QcAk/RAAECBNhhsA3EQ2+iuDByLlq2Vxmil2hO4iLfU2V9P5/OX25qW6GrL7us4vsXxvkV\nlPFLft8gX5H2GNonpN9X9B7RIogQcKwDT5xhOPTbInh8okY4HCIdC0EkDBEHFvk/ax6rGUWzvTZx\nkWXcbOyoLjwWcL3ZH0OuXbYNqpIUvC7kaBvvmpA2XQwP5hWsfmC1grUF1r3nAn9Fc2dWB2itG4us\nNQCPd90XbRXagX7L1hGpoCeDEcLn+RWhkuiUqvlmi9doc7VqYJ/3KfO3L/DhX+YNukT+VcXgBWmy\nDKYLdIrnWOVXhVt4bS4hsqsUynPkuth8iO46+N5RR8Cjz5AvfulOK9aGxFtTmg/5EHpVOsN32EEv\nXOabYwPo7/eAvdbK2mwnSAJXe3V9t2ijXfSX0G3rp5mH6/xrQy0y/OX3v8+sOLyTv2zczrANEPRS\nmqVKifmXSSQaC54ZvSVITfkiuGhJq118P16w1KfNthY4xwwUv0rRAf5npnbCfvWcA3noxVzLglDY\nMQQ9pEPYLbJPXcA5wdqWC7nRriwUniwkjTZUbsWLhgVeZIWcOpQO2hUR3Y48kUtUvDeWEW25/RZa\n9X2yOd6IHl59eA4Pfpeocsig56NngSeLFfKbz4QGzzvpZRB+BmQjXrArqifm+ZiADn64EE9pdgQw\nrZ3KiLp4/XvA1Q8REmEdUCP6PV1jzl+A9/6+arbXcgZwm++Gc832ELQydE2RinMZlmXAT8Hz4asF\nVqUpJEt3BSYLfNbqTsxxq2gt9F3tsThwKFoXkQFc602M5ivWxuBHVqy6AS29uhd057W+HLXoaSKN\np2kZCz2Fqfb2TsxhmyDow0NbW4L8aJcKeEHBqgw8fcMLo1Tz1yso4wCT8LirPIJu3PX88TKmme1v\nkvDxqMcUN4f1Qx40JU5zzQFc6rtGZ1Z3oMbRoztnRZAHa0tmuVCru3QwsUfxWLwxaP8G2yjrimlN\n/nLqUJ6jUHgwpQzjzR0Rc9uxaBWxdaE6CG2pcpLvnfg/rf9pChGDPrJS31z45eL+8AUOxPIchzc8\nDANrdBstZ1/OmKUSrEFb9czFe50/xU/LbX/6Zu6F2Qf1tLcLN+p155OusHZtiNl7CWeIdy9/hB7v\n8WbktejFvG0xNGoTLOCkkLlfe/afaXdhSrg2BWmBevMMzaLl5GvQMY/8CrjFwBN+L7GQNv9r0i3I\nKoJN7FBtf2D66W4T4ciJ8H+HAO1x4DBTOomnKLKGlm/gX+3saNjuCfqCBe2c35agWaC93VoQVGTv\nMER8g1z+2Q8b0BU8Nrifwlap87NFB3x3tMq2N7sVzhiPbFnyGA0Js5p9Rsn4vBtt3JVyic6sEXIf\nOQOIHnLtzuvQRL3F3NLKr3r89fgIulLGttvxQu0aJMmNrKPQEh3wCOYH0C7F4/PK2Ui4vcAvzPGN\naOXcbnjvpgE4ETgh7zGvMZzigUCHebaXHO1gEcvj0F9Zo4d8ne/VjJTx7CNope30gmfLDUVvKfWH\nm4x/hboPgLd/B8+2JdjfvPIFncB3hrumsEhBSPfOA7HvxL9CXEKx8fDAOyl6QmFvtTYRuI/sgvXt\nQVesxeslk41gsZDJ7ofRwjVN0d/E4c0Evm7vs7wC9NRsa+um8nAi2x+2K4IeqTmKVN9g+4LrF4xU\n581HvnzfyimKIY0XRqAR3YNrgf9njlly1KatUYyZ8tV0wm6NXHK6dvwAeP+YQjyoRsjJFfxmXKXj\nsmSAVAE+tpVck48Metz04zEuYby4KpZrb0RTNvvIdmtDe+dD0M8VdgbZd2eUx2zmR783ptaMMU1T\neKL9bvTwjAP/hy/yTj/cYfym7H3nkytnt1yd5VxdPOKslI41ksmb+57CUyfsYo71MHIkYBFeHgrQ\nz6lXI+Wsl31vzBByP/60oH2U2JiroV27obfzOfTb8TModxS6CICeZ3vhXRsfHp1AZcByBvnpSjrJ\nDQU1jxwb0Rz82fxZnA+qmaMEHj0TuNWuZuzHjZErEtq+whVUg8DKJUCAAAF2EGxXHHoh7nx0MRet\nTLHs7SXFDV4VkIlCr+E3LWMigJqpj/UmoBYuEf3i223U3tuXcuIn9yr5MbIyUeXLymOPZVzNaiZd\nwzn7BMUOeqFgHyGO5tg7yQ0Ra+XLLcAsc6wOz7rFxestWebbZxkeNfL7mKMtbfKUog6e0CCOx4GD\np57zu/fbZvWYqmuAf8c4tYBmWF8BXgNlZBgL9jc3t/Kcn5iyOasJU7OE4QsO40K5L9Mu+LsBv/Zl\nCqMTIdlbx/y1TMmRCBQ3VFj353Lu6nno6gecrMiGY4G/2D7j6LEE+uNe1Qh8iWy47IuAq74GfKuC\niuIg2jHv0VmQ40z3aeDpVnjrw+bYnwtcv2Xx4UPez7jaMD97qIoAfBViuyLoWxx77gWhI2GyeU0H\nwYdEu1hb+tiFiQRtZdh5xjZhPHFysk6719qXntjN7uxFIbhAd0YHuw+ZVWLahY7uXCeRjKvAEnXJ\nc/efLDrxgdUyjseTl4MnRqlBp6lbJZ4MpAVNDPvN1i+arLEPbQj6ONFhByKilaJ5IpfupKLTKHZX\nKe1wYjHVa+2gmIXj0fQ5DuyBT/EXgmVz4Za5ePq5B/JcOUtBATddwLoPVmKtpFWKJ+NzkBwGzPTO\nYowgpRK3R1UwJN8QcCLaR7cUJqNjzrwG3D30qjZuBkLQYt5xt43s0gvUwSLz0C+Bnmi/R/btXFUs\naHIhXAPtCe3/5J+f22C+QO8uwrdv218fe7uB0Sfqn8EbcCloaOKjX2pinzxqmzhSmyJ3p7Tb1Lev\nvJ0c06ohICDopdBUA/uHsxx5rWg+qhwvBWQHqZ85NAECKkJ7+2KszL31sxdQv0RLj999PQmRJTll\nszJ0B5N519ero8ac0M4BS8nFkWYbRuuS0wqeNQPtOLS4cRN6DFqCrvwP4njXi/kZEpw8pejKPkV/\nXB9bkifos+qqOeiFwwCeZ+AKtI3+LAYHL5iOJvC3GCq/tL3aAOa91DfmEvQavMf043GKGZ1Wh7l4\nStts37DxcmAwcc9+yndHoHY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+dNYD36pNnYYr3DI1EGpLUjEc2STl0S0u0bC0asS1oaYXevfBFafK+myZoBep+zbnv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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"seovVTgQyuI6","colab_type":"code","outputId":"a5bb64ea-1b74-4fb1-b44f-3adeb4080878","executionInfo":{"status":"ok","timestamp":1567697395170,"user_tz":240,"elapsed":1185,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["files.sort()\n","files"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['image_0.png', 'image_orig_0.png']"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"3glOy35v60PU","colab_type":"code","colab":{}},"source":["# test the model\n","# output = model(img)\n","# img.shape\n","# c.shape\n","# img.shape\n","# output.shape\n","# c.shape\n","cp -r ./dc_img_cifar/ ./drive/My\\ Drive/AutoEncoderProject"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Xh8fj8PL7TrC","colab_type":"code","outputId":"c103b6d6-f376-4d21-e1be-6c1773e318d1","executionInfo":{"status":"ok","timestamp":1567697518606,"user_tz":240,"elapsed":4805,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","def convert_to_imshow_format(image):\n","    # first convert back to [0,1] range from [-1,1] range\n","#     image = image + 0.5\n","#     image = image + 0.5\n","    image = image.numpy()\n","    # convert from CHW to HWC\n","    # from 3x32x32 to 32x32x3\n","    return image.transpose(1,2,0)\n","\n","# dataiter = iter(trainloader)\n","# images, labels = dataiter.next()\n","\n","# fig, axes = plt.subplots(1, len(images), figsize=(12,2.5))\n","# for idx, image in enumerate(images):\n","#     axes[idx].imshow(convert_to_imshow_format(image))\n","#     axes[idx].set_title(classes[labels[idx]])\n","#     axes[idx].set_xticks([])\n","#     axes[idx].set_yticks([])\n","\n","import matplotlib.pyplot as plt\n","\n","for i in range(10):\n","  \n","  f, axarr = plt.subplots(1, 3)\n","\n","  c = img[i].cpu().detach()\n","  print(torch.min(c), torch.max(c))\n","#   c = c.reshape([28,28])  \n","#   c = to_img_3(c,32,32)\n","#   c = c.squeeze()\n","#   import pdb; pdb.set_trace()\n","#   c = c.reshape(32,32,3)\n","  axarr[0].imshow(convert_to_imshow_format(c))\n","  axarr[0].set_title('image')  \n","  \n","  aaa= img[i].reshape(1,3,32,32)\n","  output_hr, code = model(aaa)\n","\n","  c = code.cpu().detach()\n","  print(c.shape)\n","#   c = c.reshape([32,32])\n","#   axarr[1].imshow(c.numpy())\n","#   axarr[1].set_title('code')   \n","  \n","  \n","  \n","  a = output_hr #reshape([32,32])\n","  a = a.cpu().detach()  \n","  axarr[2].imshow(convert_to_imshow_format(a.squeeze()))\n","  axarr[2].set_title('high-res recon')  \n","\n","  \n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"stream","text":["tensor(-1.3862) tensor(1.0084)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.4814) tensor(2.1309)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.6561) tensor(1.6395)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.4106) tensor(1.5176)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.3873) tensor(1.5828)\n","torch.Size([1, 16, 24, 24])\n"],"name":"stdout"},{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"stream","text":["tensor(-1.6293) tensor(2.1309)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.3544) tensor(2.1309)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.5809) tensor(1.5193)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.6777) tensor(1.7747)\n","torch.Size([1, 16, 24, 24])\n","tensor(-1.9101) tensor(1.6790)\n","torch.Size([1, 16, 24, 24])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4Ee4c2f1V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Q1yiUi9gIvlTQX+Bfg2HZ3EBHLgeUAkqI5BTBcM3JKIqcUWkd45JTIHamc50epuiVc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\nAABJRU5ErkJggg==\n","text/plain":["<Figure 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size 432x288 with 3 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m8QyeEkITQq0MXkVoRWS0iL4nIiyJyebh+oog8LCKvhv9WDH13CSE9wfFa2GQjuXQA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\nIdChE0JIQqBDJ4SQhE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Y218wazaxx0KBBxYqLSAVUtMsFu14DtsXTx568R1g+5PQQjwu39Ky556/uzffkbcpN\nNotlLr67/7tz8WGXfDvsoveeuXj/oeGktP+gMPpl3dPh1qKH7gnPTrrnwTDl7a5V4YGuADD8/R9E\nId0mhHrsWvqPgmVqwAgA/oD6IppR+K74RpQhAOaTnKwLo/WoCaF1uVZ2GwrHrVmWEueZmotmNw4L\niye6886hrtNl24shvjw8TL2eVTahS80g2RPAqQA+1bLMzLbA3SRL8i4A31AyF+ka1OVSv5YCmGdm\nS0jOITm52hUSkY6p4hm6e9Tsuitc3MHNbg7dGw+cNbVgkQ7dIjewX97LNXc+UbDYmHH75uLnarDL\nxcz2cfF5KWWOqliFRHKTQQLw3wn1/bBkOkMXEckIJXQRkYzQRdE07jcz+IT35eJNDb3yiu2Ynz/6\npsVzjxTuihER6Sw6QxcRyQgldBGRjFBCFxHJiLrvQ9/3lE/m4gPH9MnFI4funouXPhsm3frjg6m3\nueUb6oY3rm5//fL1S7zWM1pFJNAZuohIRiihi4hkRN13uSybd3OIy7nhhzpj2KK6WEQknc7QRUQy\nQgldRCQjlNBFRDJCCV1EJCOKJnSSvUk+SPJxkktIzo6XjyH5AMnlJH8TPzBBRKpI7bW+lXKG/iaA\nY8zsvQAOBHAiycMRPRX+MjMbC2ATgGmdV01pv97hp2H36EdfzLJM7bWOFW3ZFtkWv+wR/xiAYwC0\njPm7DsDHOqWGIlIytdf6VtKpGsnuJB8DsB7AQgDPAdhsZi1PWF4NYHjKe6eTXExSzx0RqYBytdft\n27dXpsJSNiXdWGRmOwEcSLI/gN8BGF/qDsxsLoC5AEDS2lPJutZ/cIh7985f19C98Hu27Qxxc96K\n6J/tTeWomdSocrXXYcOGqb12MW3qTDWzzQAWATgCQH+SLR8IIwCsKXPdRKQD1F7rTymjXAbFn/Qg\n+Q4AxyN6YvwiAC1TFU5FB5+9LCIdp/Za32jW+rcqkgcguojSHdEHwDwzm0PyXQBuArAHgEcBnG5m\nbxbZ1gYArwPYWIa6dzUDUTvHvbeZDSrXxhobG23xYl0iqTaSDwP4V5S3va5Ebf3tVkqtHXNJbbZo\nQi83kovNrLGiO60BWT5uJfTaQPLhzvgby/LfbpqueswakCwikhFK6CIiGVGNhD63CvusBfV63NL1\n1ePfbpc85oon9Hica92p1+OWrq8e/3a76jGry0VEJCOU0EVEMqKiCZ3kiSSXxVN4zqzkviuF5EiS\ni0g+HU9fena8fA+SC0k+G/87oNp1FWlNPbRXIFtttmIJnWR3AJcDOAnARACnkZxYqf1XUDOAGWY2\nEcDhAL4UH+dMAH8xs3EA/hK/FqlJddRegQy12Uqeob8PwHIze97MdiC6a21KBfdfEWa21sweiePX\nEN12PRzRsV4XF9P0pVLr6qIMcCv4AAAFKElEQVS9Atlqs5VM6MMBrHKvU6fwzAqSowEcBOABAIPN\nbG28ah2AwSlvE6kFdddega7fZnVRtJOQ7APgtwC+ZmZb/TqL5lvQ1KQiNSQLbbaSCX0NgJHudWan\n8CTZA9Efxq/M7JZ48cskh8brhyJ6+EA16tbqhS6SX48vDj1B8i8k965GPaXq6qa9ArXdZtuikgn9\nIQDj4ofV9gRwKoD5Fdx/RZAkgKsALDWzS92q+YimLQWqNH1piRe6HgXQaGYHIHpk2fcqW0upEXXR\nXoHabrNtVbGEHj/+6ssAbkd00WGemS2p1P4r6AMAPgPgGJKPxT+TAFwE4HiSzwI4Ln5daUUvdJnZ\nIjNrefbY/YjOzKTO1FF7BWq7zbZJSY+gKxczWwBgQSX3WWlm9jcATFl9bCXrUkChC12HtVJ+GoA/\nFVpBcjqA6QAwatSoctVPakg9tFeg5ttsm+iiqBRE8nQAjQC+X2i9mc01s0Yzaxw0qGzPyhCRDqjo\nGbpUXUkXukgeB+DbAD5U7Kk2IlI7dIZeX4pe6CJ5EIArAEw2s5q/qi8igRJ6HUm70EVyDsnJcbHv\nA+gD4H/ji0OZHNkgkkXqcqkzhS50mdl5Lj6u4pUSkbLQGbqISEYooYuIZIQSuohIRiihi4hkhBK6\niEhGKKGLiGSEErqISEYooYuIZIQSuohIRiihi4hkhBK6iEhGKKGLiGSEErqISEYooYuIZIQSuohI\nRiihi4hkhBK6iEhGKKGLiGSEErqISEYooYuIZIQSuohIRiihi4hkhBK6iEhGKKGLiGSEErqISEYo\noYuIZIQSuohIRiihi4hkhBK6iEhGKKGLiGSEErqISEYooYuIZIQSuohIRiihi4hkhBK6iEhGKKHX\nGZInklxGcjnJmQXW9yL5m3j9AyRHV76WItIeSuh1hGR3AJcDOAnARACnkZyYKDYNwCYzGwvgMgAX\nV7aWItJeSuj15X0AlpvZ82a2A8BNAKYkykwBcF0c3wzgWJKsYB1FpJ0aql0BqajhAFa516sBHJZW\nxsyaSW4BsCeAjb4QyekApscv3yT5VKfUuHIGInGMXdC+1a6AVJcSurSLmc0FMBcASC42s8YqV6lD\nsnIM1a6DVJe6XOrLGgAj3esR8bKCZUg2AOgH4JWK1E5EOkQJvb48BGAcyTEkewI4FcD8RJn5AKbG\n8ScB3GlmVsE6ikg7qculjsR94l8GcDuA7gCuNrMlJOcAWGxm8wFcBeCXJJcDeBVR0i9mbqdVunJ0\nDNLlUSdfIiLZoC4XEZGMUEIXEckIJXQpWRamDSjhGM4guYHkY/HPWdWoZ2tIXk1yfdrYf0Z+HB/j\nEyQPrnQdpTqU0KUkWZg2oMRjAIDfmNmB8c+VFa1kaa4FcGIr608CMC7+mQ7gZxWok9QAJXQpVRam\nDSjlGGqemf0V0QikNFMAXG+R+wH0Jzm0MrWTalJCl1IVmjZgeFoZM2sG0DJtQK0o5RgA4OS4q+Jm\nkiMLrK91pR6nZIwSuki+WwGMNrMDACxE+MYhUvOU0KVUWZg2oOgxmNkrZvZm/PJKAIdUqG7lVMr/\nlWSQErqUKgvTBhQ9hkRf82QASytYv3KZD+Cz8WiXwwFsMbO11a6UdD7d+i8l6cRpAyqmxGP4KsnJ\nAJoRHcMZVatwCpI3AjgKwECSqwHMAtADAMzs5wAWAJgEYDmA7QDOrE5NpdJ067+ISEaoy0VEJCOU\n0EVEMkIJXUQkI5TQRUQyQgldRCQjlNBFRDJCCV1EJCP+P+6MnaCdqWdjAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"VbZ5myx7DJoU","colab_type":"code","outputId":"3b744924-640f-4153-f73c-4cef45598776","executionInfo":{"status":"ok","timestamp":1567697446849,"user_tz":240,"elapsed":1214,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["c.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 16, 24, 24])"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"rqTJojLb7WWT","colab_type":"code","outputId":"0932a84a-f9a5-4acd-d281-0a65b27fc8e1","executionInfo":{"status":"ok","timestamp":1563171004126,"user_tz":240,"elapsed":406,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# output.shape\n","# c.reshape(32,32,3).shape\n","# c.shape\n","# code.shape\n","cc = img[i].cpu().detach()\n","torch.max(cc)\n","# cc.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(1.6790)"]},"metadata":{"tags":[]},"execution_count":180}]},{"cell_type":"code","metadata":{"id":"iWT7A9Hk7YO4","colab_type":"code","outputId":"90d54bdb-e401-48e8-c754-8282c5a2d92f","executionInfo":{"status":"error","timestamp":1562897148916,"user_tz":240,"elapsed":876,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":283}},"source":[""],"execution_count":0,"outputs":[{"output_type":"error","ename":"RuntimeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m<ipython-input-65-c1e62e9c00d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-1-b3a17bfc64d2>\u001b[0m in \u001b[0;36mto_img\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mRuntimeError\u001b[0m: shape '[784, 1, 28, 28]' is invalid for input of size 784"]}]},{"cell_type":"code","metadata":{"id":"tURLOuOs7ehU","colab_type":"code","outputId":"0722ac52-2c21-46e4-b0ef-14c74bb9c9a4","executionInfo":{"status":"ok","timestamp":1567707414662,"user_tz":240,"elapsed":25828,"user":{"displayName":"Ali Borji","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCyPinJVGcT7jgXnUcueY9m7IcAP1_bp0QKeF8QQ=s64","userId":"01590204968742485374"}},"colab":{"base_uri":"https://localhost:8080/","height":595}},"source":["import torchvision\n","\n","num_epochs = 1\n","\n","def resize_tensor(input_tensors, h, w):\n","  final_output = None\n","  batch_size, channel, height, width = input_tensors.shape\n","#   import pdb; pdb.set_trace()\n","  input_tensors = torch.squeeze(input_tensors, 1)\n","  \n","  for img in input_tensors:\n","    img_PIL = transforms.ToPILImage()(img)\n","    img_PIL = torchvision.transforms.Resize([h,w])(img_PIL)\n","    img_PIL = torchvision.transforms.ToTensor()(img_PIL)\n","    if final_output is None:\n","      final_output = img_PIL\n","    else:\n","      final_output = torch.cat((final_output, img_PIL), 0)\n","  final_output = torch.unsqueeze(final_output, 1)\n","  return final_output\n","\n","def custom_loss(output, img): #nn.MSELoss()\n","    h1, w1 = 10, 10 #output2.shape[2:]\n","#     img_lr = img.view(-1, 3, 32, 32)\n","#     img_lr = resize_tensor(img_lr.cpu(), h1, w1 )\n","# #     print(img_lr.shape)\n","#     img_lr = img_lr.resize(img_lr.shape[0]//3,3,10,10)\n","    \n","#     import pdb; pdb.set_trace()\n","#     \n","\n","#     img_lr = img_lr.reshape(-1,h1*w1)\n","#     out2 = output2.view(-1, 1, 28, 28)\n","    return nn.MSELoss()(output, img)\n","\n","\n","\n","# def custom_loss(output, img): #nn.MSELoss()\n","#     h1, w1 = 10, 10 #output2.shape[2:]\n","#     img_lr = img.view(-1, 3, 32, 32)\n","#     img_lr = resize_tensor(img_lr.cpu(), h1, w1 )\n","# #     print(img_lr.shape)\n","#     img_lr = img_lr.resize(img_lr.shape[0]//3,3,10,10)\n","    \n","# #     import pdb; pdb.set_trace()\n","# #     \n","\n","# #     img_lr = img_lr.reshape(-1,h1*w1)\n","# #     out2 = output2.view(-1, 1, 28, 28)\n","#     return  nn.MSELoss()(output1, img_lr.cuda()) + nn.MSELoss()(output2, img)\n","\n","\n","\n","#  --------------------\n","\n","class autoencoder_two_branch(nn.Module):\n","    def __init__(self):\n","        super().__init__()        \n","        self.encoder = nn.Sequential(\n","            nn.Conv2d(3, 6, kernel_size=5),\n","            nn.ReLU(True),\n","            nn.Conv2d(6,16,kernel_size=5),\n","            nn.ReLU(True),\n","            nn.Conv2d(16,16, kernel_size=5),\n","            nn.ReLU(True),\n","            nn.Conv2d(16,1, stride = 2, kernel_size=5),\n","            nn.ReLU(True)        \n","        )\n","        \n","        self.decoder = nn.Sequential(             \n","            nn.ConvTranspose2d(1,16,kernel_size=5),\n","            nn.ReLU(True),            \n","            nn.ConvTranspose2d(16,16,kernel_size=5),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(16,3,kernel_size=5),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(3,3,kernel_size=5),\n","            nn.ReLU(True),            \n","            nn.ConvTranspose2d(3,3,kernel_size=5),\n","            nn.ConvTranspose2d(3,3,kernel_size=5),\n","            nn.ReLU(True),            \n","            nn.Sigmoid())        \n","\n","    def forward(self, x):\n","        z = self.encoder(x)\n","#         x1 = self.decoder1(z)  # low res\n","        x = self.decoder(z)  # high res    \n","        return x, z\n","\n","\n","  \n","model = autoencoder_two_branch().cuda()\n","from torchsummary import summary\n","summary(model, (3, 32, 32))    \n","\n","batch_size = 32\n","\n","criterion = nn.MSELoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,\n","                             weight_decay=1e-5)\n","\n","for epoch in range(num_epochs):\n","    for data in dataloader:\n","        img, _ = data\n","        \n","#         import pdb; pdb.set_trace()\n","#         img = img.view(img.size(0), -1)\n","        img = Variable(img).cuda()\n","        # ===================forward=====================\n","        output, _ = model(img)\n","        loss = custom_loss(output, img)\n","        # ===================backward====================\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","    # ===================log========================\n","    print('epoch [{}/{}], loss:{:.4f}'\n","          .format(epoch+1, num_epochs, loss.data))\n","    if epoch % 10 == 0:\n","        pic = to_img_3(output.cpu().data, 32, 32)\n","        save_image(pic, './dc_img_cifar/image_{}.png'.format(epoch))\n","        pic_orig = to_img_3(img.cpu().data, 32, 32)\n","        save_image(pic_orig, './dc_img_cifar/image_orig_{}.png'.format(epoch))\n","        \n","\n","torch.save(model.state_dict(), './conv_autoencoder_cifar_2br.pth')\n","torch.save(model.state_dict(), './drive/My Drive/AutoEncoderProject/conv_autoencoder_cifar_2br.pth')\n","\n","  "],"execution_count":0,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1            [-1, 6, 28, 28]             456\n","              ReLU-2            [-1, 6, 28, 28]               0\n","            Conv2d-3           [-1, 16, 24, 24]           2,416\n","              ReLU-4           [-1, 16, 24, 24]               0\n","            Conv2d-5           [-1, 16, 20, 20]           6,416\n","              ReLU-6           [-1, 16, 20, 20]               0\n","            Conv2d-7              [-1, 1, 8, 8]             401\n","              ReLU-8              [-1, 1, 8, 8]               0\n","   ConvTranspose2d-9           [-1, 16, 12, 12]             416\n","             ReLU-10           [-1, 16, 12, 12]               0\n","  ConvTranspose2d-11           [-1, 16, 16, 16]           6,416\n","             ReLU-12           [-1, 16, 16, 16]               0\n","  ConvTranspose2d-13            [-1, 3, 20, 20]           1,203\n","             ReLU-14            [-1, 3, 20, 20]               0\n","  ConvTranspose2d-15            [-1, 3, 24, 24]             228\n","             ReLU-16            [-1, 3, 24, 24]               0\n","  ConvTranspose2d-17            [-1, 3, 28, 28]             228\n","  ConvTranspose2d-18            [-1, 3, 32, 32]             228\n","             ReLU-19            [-1, 3, 32, 32]               0\n","          Sigmoid-20            [-1, 3, 32, 32]               0\n","================================================================\n","Total params: 18,408\n","Trainable params: 18,408\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.01\n","Forward/backward pass size (MB): 0.54\n","Params size (MB): 0.07\n","Estimated Total Size (MB): 0.62\n","----------------------------------------------------------------\n","epoch [1/1], loss:0.9813\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3OKtBnfR-P8q","colab_type":"code","colab":{}},"source":["#     h1, w1 = 10, 10 #output2.shape[2:]\n","#     img_lr = img.view(-1, 3, 32, 32)\n","#     img_lr = resize_tensor(img_lr.cpu(), h1, w1 )\n","#     img_lr = img_lr.resize(32,3,10,10)\n","# #     \n","\n","# #     img_lr = img_lr.reshape(-1,h1*w1)\n","# #     out2 = output2.view(-1, 1, 28, 28)\n","#     return  nn.MSELoss()(output1, img_lr.cuda()) + nn.MSELoss()(output2, img)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"WFn329Kupatp","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XV8Mf0c2NbxV","colab_type":"code","outputId":"7144cbc4-5c4d-4285-b8be-06b7d6dd0655","executionInfo":{"status":"ok","timestamp":1563166290512,"user_tz":240,"elapsed":876,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# output2.shape\n","# img2 = img.view(-1, 1, 28, 28)\n","# img2.shape\n","# batch_size\n","\n","z_code = torch.randn(100, 20).cuda()   #for vae\n","evaluate_x[:,None,...].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["128"]},"metadata":{"tags":[]},"execution_count":128}]},{"cell_type":"code","metadata":{"id":"HRohKtLb115v","colab_type":"code","colab":{}},"source":["mkdir dc_img2"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CKctBQr2NgE9","colab_type":"code","colab":{}},"source":["# img[:,::2].shape\n","img2 = img.reshape(-1, 1, 28, 28)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lJPdhKVDOJMm","colab_type":"code","outputId":"5809a6b3-588c-4abf-fe88-04064dda4305","executionInfo":{"status":"ok","timestamp":1563136963753,"user_tz":240,"elapsed":385,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import math\n","math.sqrt(392)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["19.79898987322333"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"X9kOjzm4OR1V","colab_type":"code","outputId":"30cee694-f209-41a5-b138-20cf1f38d786","executionInfo":{"status":"ok","timestamp":1563137162434,"user_tz":240,"elapsed":443,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["img2.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 1, 28, 28])"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"2HOF7VGPPDrF","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"R887Qsu8QCYN","colab_type":"code","colab":{}},"source":["a = resize_tensor(img2.cpu(), 10, 10)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"t9KnopZ0QIHo","colab_type":"code","outputId":"cfa813a9-087b-4ff4-f699-dd305b5fd0ce","executionInfo":{"status":"ok","timestamp":1563137473668,"user_tz":240,"elapsed":923,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["a.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([128, 1, 10, 10])"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"id":"s-4czEtpQPih","colab_type":"code","outputId":"73d8aa45-6af1-4596-b82d-2c1076052c1b","executionInfo":{"status":"ok","timestamp":1563137538170,"user_tz":240,"elapsed":1050,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import matplotlib.pyplot as plt\n","plt.imshow(a[0,0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc330b6b438>"]},"metadata":{"tags":[]},"execution_count":52},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOTKYijsLAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"nakb9ezsQSWU","colab_type":"code","outputId":"aa81661f-417f-4530-a344-26e71a6d5b2e","executionInfo":{"status":"ok","timestamp":1563137566515,"user_tz":240,"elapsed":403,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(img2.cpu()[0,0])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc3302a7588>"]},"metadata":{"tags":[]},"execution_count":55},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"8F2iykRBQigc","colab_type":"code","outputId":"131882d9-3bdf-465d-b816-92a7dc499677","executionInfo":{"status":"ok","timestamp":1563169136092,"user_tz":240,"elapsed":8560,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import cv2\n","import matplotlib.pyplot as plt\n","# pallete = np.zeros(10*28,28*4)\n","\n","for i in range(10):\n","  \n","  f, axarr = plt.subplots(1, 6, figsize=(15,15))\n","\n","  c = img[i].cpu().detach()\n","#   c = c.reshape([28,28])  \n","#   c = to_img_3(c,32,32)\n","#   c = c.squeeze()\n","#   import pdb; pdb.set_trace()\n","#   c = c.reshape(32,32,3)\n","  axarr[0].imshow(convert_to_imshow_format(c))\n","  axarr[0].set_title('image')  \n","  \n","  aaa= img[i].reshape(1,3,32,32)\n","  output_lr, output_hr, code = model(aaa)\n","\n","  \n","  \n","#   c = code.cpu().detach()\n","#   print(c.shape)\n","#   c = c.reshape([20,20])\n","#   axarr[1].imshow(c.numpy())\n","#   axarr[1].set_title('code')   \n","  \n","  a = output_lr# .reshape([10,10])\n","  a = a.cpu().detach()\n","  axarr[2].imshow(convert_to_imshow_format(a.squeeze()))\n","  axarr[2].set_title('low-res recon')  \n","\n","  \n","  a = output_hr #reshape([32,32])\n","  a = a.cpu().detach()  \n","  axarr[3].imshow(convert_to_imshow_format(a.squeeze()))\n","  axarr[3].set_title('high-res recon')  \n","\n","  \n","#   interpol_img = cv2.resize(output_lr.cpu().detach().numpy().squeeze(), (32,32))\n","#   interpol_img.shape\n","  aa = output_lr.cpu().detach().numpy().squeeze()\n","  aa = aa.transpose(1,2,0)\n","  interpol_img = cv2.resize(aa, (32,32))  \n","#   interpol_img = resize_tensor(aa, 100, 100)\n","#   a = interpol_img.reshape([100,100])\n","#   a = a.cpu().detach()\n","  axarr[4].imshow(interpol_img)\n","  axarr[4].set_title('interpolated recon') \n","  \n","  # super resolution\n","#   a = output_hr.reshape([28,28])\n","#   a = a.cpu().detach()\n","#   axarr[5].imshow(a.numpy())\n","#   axarr[5].set_title('high-res recon')   \n","  \n","#   pallete[:i*28, :28] = c \n"," \n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA24AAANSCAYAAAAd6Z2xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncGq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11n7aYb1Wum4Ktgw+OzvutHt6gHUx24nx9ddfbwfliI08dnF2se8Am2ndH04CsMa0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\nG7fvQN+27K03/7+uvHP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cWGvPSnJra+2DrbWlJK9K8sKjTPevkvybJIc3snIAAGvIeQ8b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\n","text/plain":["<Figure 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nTk3zkMPOurqrtVbV9165dK101TgOttcE/Vkeb/uczAU5RghuwJlXVWUnemuTHWmu7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\ngg==\n","text/plain":["<Figure 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size 1080x1080 with 6 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TO5FbJJuXGDzmpNXnNMbPVZrbWzNaeOM7QAwCAshr02FV43Ors7Cxp4xC1YZ/39PT0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\n3AAAAMoJNwAAgHLCDQAAoJxwAwAAKPfftn9WNV+wfo8AAAAASUVORK5CYII=\n","text/plain":["<Figure 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EiLpubZgEc0kyeSSllMiIO5JnAkXDu4ljZd4AFdcx33EopU5n5joj4TEQsiYg/KKVc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\n7voX4MbIAtEoxuhoL3966qTRxr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Kqes7E/E+vqkvG3z/XVNXPb+dxX1hV/1ZVv1dVX0/yyvHtPzH+/rqlqt5bVSdN3OchE897Q1X9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\nf+3lg9qYNoDl9+3N38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B0TnADAADonOAGAADQOcENAACgc4IbAABA5wQ3AACAzgluAAAA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\nBgAAUE64AQAAlBNuAAAA5X4DpsRuhJvUF34AAAAASUVORK5CYII=\n","text/plain":["<Figure 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t2b5ubvuvBnYN5H/mDXxmUb/7ilw3KL/yq+XHfNl1wxmHV48FtD89Nz0x99Fu33j4o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\n","text/plain":["<Figure 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ixR1fV26vqy1X1yap6/sTyz6mqj4/3PzdW1Y8usN6XVNVfVdUvVtWXkrxq/Pg/Ge+/bq+qt1XV\nIyae85iJ1/1iVf3EMt775VV1Q1W9Yry/vKmqvu+YPuyN7+uq6sNVta+q3lxV25IHN3+sqidU1QfG\n3+XvjJcdNCFb7uc68T38WFXdnOTXx49/e1V9sKruqKq/rqrHTTznx8bb0J3j7ewZC6z7dVX1S1X1\n1qq6O8nfW+pYVlXPG7/uV6rq01X1rPHj51bVVeNt7Nqq+v6J57yqqt5SVb8xrtPHqmrD3syoJq6i\nLfbequoNSR6e5H9W1V1V9a/Hjz95/J3dUVUfqolml1X1zqr62ar6qyT7k3zV+LGfq6q/GX/uf1hV\nZ0w857nj171jvOzXLFDvJ1XVu8fL3VRV/7VGt79PVb1rvNiHxnX9nvHji21nj6+q94/f95uTbFvk\nM7Pfgghuy/EdSd6Q5PQkH8jolq6bkpyX5KeT/MrEsrck+fYkpyX5viS/WFVPSJLxwelfJvnmJI/M\neDyPCa/OaFyPrxvPPy/JT67FGwJW1f+T5MkZ/e1+bZInJfk343lXZ/5v/bKMxu152kT56iXW/Z1J\nvj7JpVV1SpK3J/ntJA/LaKyg/1ZVl46XfW2Sl7XWTk3y2CR/vsh6v35cl7OS/GxVPS/JTyT5riS7\nk/xFkjcmSVWdmuRPk/xJknMz2j/92TLee5KcnWRnRvuzlyZ5TVWdvsR7Pp49P8mzklyU5HFJXjK9\nwPhE+PeTvC7JGRl9D39/arGVfq5nj9f1iCRXVNXjk/xakpcleWhGx7Grxie0l2R0a/O/O96Wnpnk\nc4us+/9K8rNJTk3yl1nkWFZVT0ryG0n+VZJdGf0tPLDuN2U0SPG5Sf5Bkn9XVU+feJ3njpfZldEY\nWf91kTptNEd8b621703y+STf0Vrb0Vr791V1XpI/TvIzGX2nP5rkd6tqcrTm701yRUbfyXXjx/5x\nkn+S5Jwkh5L85ySpqkdltI39SEZ/+2/NKChuPUI970/yL5KcmeQbkjwjyf89rusD+7WvHdf1zUts\nZ1szGtvsDeP38TtJvnuJz8l+C1pr/k39y+hA8s0Z/aLz9onHvyPJXUlOGpdPTdKS7FpgPX+Q5OXj\n6V9L8nMT8x45fu4jk1SSu5N89cT8b0jy2Vl/Fr38e+ITn9g4fiXZ2zrYzpb774F9xHj600meMzHv\nmUk+N55+RpIPj6f/JMk/TfKecfnqJN+1yGu0JE+fKH9Pkr+YWuZXkvzUePrzGZ0gnbZE3V+S5PNT\nj/2vJC+dKG/K6Nf6RyR5YZIPLLCuxd775UnuSbJ5Yv4tSZ486+9vhtvMiybK/z7JL098VjeMp5+W\n5MaMh+sZP/aXSX7maD7X8fIHk2ybeOyXkvzbqeU+mdGPCY8cr++bk2xZ4j29LslvTJQXPZaNt9df\nPMJ6LsgoFJw68djPJXndePpVSf50Yt6lSe6Z9Xd6jNvCA/uPRd/b5LLj8o8lecPU+t6W5MXj6Xcm\n+emp+e9M8uqp1ziY5KQk/29G4449MG/TePu7/EivP7XeH0ny+xPlluSRy9zOnpbkC1Pb+V8/sJ0f\n4bVekhNsv+W85/iWozzvccVtaV+cmL4nyW2ttfsnykmyI0mq6tlV9Z7xZfk7kjwno1+mktEvPtdP\nrGtyeneS7UmuGTcnuCOjk7zJX9CAPp2b+V+1M54+dzz97iSPqqqzMvp19zeSXFBVZ2b0K++7ksN3\nmbtr/O+bJtY1uZ94RJKvf2AfMd5P/KOMfh1ORr9WPyfJdTVqovkNi9T5+qnyI5L8p4n1fjmjk/Dz\nMjqp/vRRvPck+VIbDU77gP0Z7y9PUDdPTC/0WZyb5Mbxgf0B09/XET/Xqnr4xHZ018T8W1trBybK\nj0jyiqlt6YIk57bWrs3ohPxVSW6pqjdV1eR3Om0lx7KFtqVzk3y5tXbnxGPXZbT9PWD6s9tWx08/\np5W8t0ck+YdT3903ZnQl7QHT28v0Y9cl2ZLR+cngb7i1Njde9rxMqVFXkT+qqpur6itJ/l3mz3EW\nqusRt7MceTu/7kgrWeR92W9xwhHcVsm4ffTvJvkPSc5qre3KqMlBjRe5Kcn5E0+5YGL6toxC4GNa\na7vG/3a21uwooH9fyOgE4gEPHz+W1tr+JNckeXmSj7bWDmb0q/K/TPLp1tpt4+Ue00bNi3a01v5i\nYl3TJ+9XT+wjdo2X/+fjdbyvtfa8jJpR/kGStyxS5zZVvj6jZpaT6z65tfbX43lftdL3zlG7Kcl5\nVVUTj12w0MKTWmufn9iOJo8fR/q+f3bq+97eWnvjeD2/3Vr7xoy+25bk5xd72YnppY5l1yf56iOs\n4wtJzhg3b3vAwzO68nOiO9J394ap7+6U1tqrF3lOMtyGHp7kvoy+r8Hf8Hi7uyBH/ux/KcnfJrm4\ntXZaRs0U6wjLTdZ1oe3sSNv5wxdZ15Hel/0WJxzBbfVsTfKQJLcmOVRVz07yrRPz35Lk+6rqa6pq\ne0bNE5Ic/oXrv2fUJ+5hSVJV51XVM9et9sDRemOSf1NVu8dX0n4yyW9OzL86oz5DD/Rne+dUebn+\nKKOrd99bVVvG//7ueJ+ytar+UVXtbK3dl+QrSeZWsO5fTvLjVfWYJKmqnVX1Dyde95yq+pFx35RT\nq+rrl/neWbl3Z9Rs8AeravO4H8+TVvk1/nuSf1ZVX18jp1TVt42/20uq6unjHyMPZBTElrUtLeNY\n9tqMjoPPqKpN43mPbq1dn9EPGj9XVdtqdAOLl8a2lIxa/UwGkN9M8h1V9cyqOmn8eV1eVecv8PwH\nvKiqLh2ff/x0kv8xbj30liTfNv5OtiR5RZJ7M/o+pp2a0b7lrqp6dJJ/vkRdF9zOMtrODyX54fG+\n7Luy8u3cfosTjuC2SsZNPH44o53g7Rl12L5qYv7/yqgz8DuSXJvkPeNZ947//7EHHh83QfjTJMb1\ngf79TJK9ST6c5CNJ3j9+7AFXZ3TC864Fyssy3sd8a0Y3JflCRs2rfj6jH4yS0Q0JPjfef/yzjJpR\nLnfdvz9e15vGz/9okmdPvO63ZNTH9+aM7nD598ZPXeq9s0Ljq7LflVFwuSPJizI6Cb13seet8DX2\nJvn+jG6CcXtGx56XjGc/JKMbjNyW0ff9sCQ/voLVL3gsa639TcY37kqyL6O/hQeufLwwyYUZbdu/\nn1HfzT89mvd3nPm5jELGHVX1o+OQ+8BNOW7N6MrSv8rS53NvyKhP4s0Z3b3xh5OktfbJjLax/5LR\nd/4dGd0M5eAR1vGjGZ3b3JlRKHvz1PxXJXn9uK7PX2w7m9jOX5JRE8fvSfJ7S30Yk+y3OBHVsHkx\n66VGt9v9aJKHTLWl5gj27NnT9u7dO+tqsEaq6prW2oa9vTespap6b0Y3Mvn1WdeFjaeq3pnkN1tr\nvzrrurB8znuOb0d73uOK2zqqqr8/vmR/eka/Ev1PoQ2ASVV1WVWdPW4q+eKMhg74k1nXC4DZEtzW\n18syuq3spzPqwzDdPhwALknyoYyaSr4iyT9ord002yoBMGvHy610N4TW2rNmXQcA+tZauzLJlbOu\nB8eH1trls64DsDqO6YpbVT2rqj5ZVddW1StXq1IAsFYcuzhath1glo76iltVnZTkNRndteeGJO+r\nqqtaax9f6Dlnnnlmu/DCCw+XJ2+LsthAIKyeld6KZrHlJ7+z6z73udx2222+Rrq0c+fOdvbZZy+9\nIBvOzTffnH379i1737PSY9fOXbuG284x3dBrsppLreekYXHz1oWfu2n49rduGT53Sy3y8dSixWXP\n69Xc1Pd1/9xwZIMPf/CDt7XWdmcZjua8Z/v27W3Xrl0rrTYbwB133JH9+/dvxD8LNrBjaSr5pCTX\nttY+kyRV9aaMblG74A7swgsvzPsm7pAzeVeOlV76W2z5lQxedKJZ6Wcz+R1NP3dy43nKHjcEpF9n\nn312fuVXfmXW1WANvOxlL1vpU1Z07Dr77LPzK1e+dv6Bucm94kpD3GT4OtLd1iftHBbPnBibuN03\nnLdteCi/4OzTBuWHnbzwoX4q8z0o5E2WTlriFHVWZ7DT38Jk+d5D9w/m7bvr7kH53DN2XbeCl1rx\nec+uXbtyxRVXrOAl2CiuvFJrZtbfsTSVPC+j8UMecMP4sYGquqKq9lbV3ltvvfUYXg4AjtmSx67J\n49a+O+5Y18rRtRWf9+zfv3/dKgcc/9b8rpKttStba3taa3t2715WawSARelnwlqaPG7t1MyNFZrc\nfrZv3z7r6gDHkWNpKnljkgsmyuePH1tQy7BxyGTTu6US5GLzp+ctta7J150eRG26OeDxNl7CSptK\nLrb85DzDuLNejqafCUxY0bHr1B078rRvesrh8oG5+b3dlk0LNytMHtRLbcrincvWq9nh9L57+nVX\ntG9/0MLrc2SYfpVDEw/cN9XH7Z777s8xWPF5D8BqOpZc8r4kF1fVRVW1NckLkly1OtUCWNDhfiat\ntYNJHuhnAsvh2MXRsu0AM3XUV9xaa4eq6geTvC2jHxZ/rbX2sVWrGcCRHamfyddPL1RVVyS5IknO\nOuus9akZ3XPs4mjZdoBZO6YBuFtrb03y1pU8Z/IS39wC00s9b9pKm5Qs9rrT5cWaUk5/eMfjaObL\nbaLqfrj0ZnIQ40suuURrXg5bybGrJTk00TzytgPzTe12Td2t8Z6prexhKzhwzWofutTrrqheD1p4\n4WevrAnm4ktPf8xbJ1520+ap7+jkbSt55SNUZeXnPQCr5XjrwgUc//QzAQBOOIIbsNHoZwIAnHCO\nx9Z9wHFMPxMA4EQ00+C22OW+pW7Tv+UYXndL7p+YHq75nnsPDsqbtmwd1mPT/Ec2N9V+/8DU6xzL\nhytRw8L0M2G9tJbcP9HF6ot3zu/pb28nD5a9/f7hMeGGU4dHucdMzH7I6lWxG8c0GsCinemW6mm3\nyAtNP3XT4oM0APRMU0kAAIDOCW4AAACdE9wAAAA6100ftwNTndgO3Tsszx26ffjcQ3cent6yefrJ\ndw/L26fatD/kzInCQwezTn5Qx4O7pso7JqanP77hsvdNzT94aP4dH5rq4fCgcew2L1oEYB0cmpvL\nl+6e79d2999eMz+9xHPvnCq/Z1C6YDjz7K8aFC++ZDj73JrvrDWrMd8e3Idt+Mh03/TJft/TvxJv\nX8m7OIYubtM92rZu1scN2LhccQMAAOic4AYAANA5wQ0AAKBz69p1qjJMivsnpm/89G2DZT/4N38+\nKN923ccH5c0H9s2vZ/+wb9mZZ58xKJ973lnD+Wedfnj64ql5pzxs2Octp0/1Qxh8ZIt/fNNjxG3Z\nPNHj4d7hvC9+edjn7eCO0wflHafOT0+nbekbYG3M3T+X/fv2L73gil0/LN48LH/q5uHsTx3Taz3m\n8NTXXnbmYM7OqSXrQR3K5juQXX31u6fmHczyPX5Q+sbLThuUT1qkI9uSXdxqaomJvnfTz9222RET\n2LjswQAAADonuAEAAHRuXZtKtgxve79/vrVjPvw3wxsl//p/+S+D8o2fHjaVPGeiueOmrdsG8266\nfTh0wMG5qZvtb5rPq5sPDW9gvPv0Uwflxz1meE/mr/nq8w9PP/KrHzGY9w1PedygfMqFw2aY+cpE\nvXbsHsw6Y+spg/Itc1NDCWS+XlsDwHq4795784XPXDvrahyjjx2e+tDVs6rDBwalv5yqx2WXXTZ8\nYLr54yq5f26RsQMAOueKGwAAQOcENwAAgM4JbgAAAJ1b1z5u0ya6muXTn/4/g3nXXze8NfLWTcN+\nbDfeNN9B7pY7Pj+Y96W5+46+UjfcMii+8yOfXnDRh0yVX/jtTxuU/8UPvGBQftxkn7hDdwzmbc5w\nSIOzNw375Z2URy9Yj6Xcvci86Q1g7ohLLT1PrwHg+DSX5MCsK3Hcu/rqYX/zyy7bMl9YorvbwgMY\nHGlZRytg43LFDdhQquqCqnpHVX28qj5WVS+fdZ0AANbaTK+4ARyFQ0le0Vp7f1WdmuSaqnp7a+3j\nSz0RAGCjcsUN2FBaaze11t4/nr4zySeSnDfbWgEArK11veJWGSbFyem5RcZaS5KTt+8clL98y5cO\nTx9Tn7ZjcNq2Yb+7a97/t4Pyz7/6dYPyM578pMPTz7z8KYN5V//1uwblP/6DPxqUn3rRow5PP/mJ\nTxrMe8LlU+PfnHfOsDzxWR7cNhwvLjseOihuO3X4OZ80MX1vFrY2I+7A4qrqwiSPT/LeI8y7IskV\nSXLWWWdNzwa68ldT5cnj2gqPMJNjwLVhn7Ytm/xeDWxc9mDAhlRVO5L8bpIfaa19ZXp+a+3K1tqe\n1tqenTt3PngFAAAbiOAGbDg7N9HgAAAgAElEQVRVtSWj0PZbrbXfm3V9AADWmuAGbChVVUlem+QT\nrbVfmHV9AADWw7r2cZtLcnCifOMXvnB4+gs3XjdY9rxzzhyU79o/HOds0475/mV110kZzty0aLkd\nmu+tNfXMnJ8tg/KZU6OX7Z74yM6Y2z6Yt/XA1kF5x6f3Dcqf/ezVh6d/54/+ZjDv7oPD0dY23Tgc\n5+3NH3nH4ek//vO9g3kPe+2bB+W5k4f1+Nh185/tbQeG4xGdde7Zg/JTvvU5g/IP/ORPHZ7+6ose\nPph3MDATT03yvUk+UlUfHD/2E621t86wTsAqmhzXbTCmW4Zd2FZqkw7ZwAZmOABgQ2mt/WXcDwcA\nOMEs2VSyqn6tqm6pqo9OPHZGVb29qj41/v/0ta0mACyfYxdHy7YD9Go5V9xel+S/JvmNicdemeTP\nWmuvrqpXjss/ttIXv2GieeRd+4fNCk/bdfKgfGhu2FTy4kfM7zPPOn3YjGLz5qm3NTXUwGc/+/nD\n07ftH97kflOGQwucNd10ctN8edO24Xo3bx42Htw6NcLB1txzePrgvtsH804+edho82sv2T0of9X2\nMyZKUw0854ZNI+emhk648Guffnh620WPG8w7uH04HMDFj3nsoHz6BfPNI6ebRk6+vRaArrwua3Ts\nYj1MDg8wNeTNMVxwX+YzXxfbDtChJa+4tdbeleTLUw8/L8nrx9OvT/Kdq1wvADhqjl0cLdsO0Kuj\nvavkWa21m8bTNycxui0AvXPs4mjZdoCZO+bhAFprLYu0lKuqK6pqb1Xtve3WW4/15QDgmC127Jo8\nbu3bt+9Ii3ACW8l5z/79+9exZsDx7mjvKvnFqjqntXZTVZ2T5JaFFmytXZnkyiR5/J49bbKf1IE7\n5w+IO7YO+7BtP/XQoDx317C8aaLmZz5seFv+Q4eGy9584xcH5c0TwwFs2zGs79z2YZ+2AzuH/Y/v\n2jqfdbcOu5Zlx+bh6x6cG/YK23/aOYend1506WDeVz1h2Ldsx/kXDMqbTj9vvs7bhn3Ydj10+MPf\nKWcM+8dtP/0hh6dPG1b5Qcl9qlteDi0wnQw3Hrf4AzaAZR27Jo9bl1xyiS68nZn+Qh50/GkLf2Vz\nR/9tHtV5z7nnnmv7AVbN0V5xuyrJi8fTL07yh6tTHQBYM45dHC3bDjBzyxkO4I1J3p3kkqq6oape\nmuTVSb6lqj6V5JvHZQDogmMXR8u2A/RqyaaSrbUXLjDrGatcFwBYFY5dHC3bDtCro+3jdtQm+1Dt\n/8p8p91Dd31psNy2ubunysMeVvv3z4+JtmnrlgXnJcnU7Fz8iF2Hp7fvGPYXm74EuenQcF07ts7X\nY9vUyGY7dgx7kF1wyeMH5a97+t8/PP2Yp33HYN72s84clKf7k21aYPpI5WmT86fHYluJY76TDQAs\nx0OfsOCspftU1xEnk+QkBzJgA7MLAwAA6JzgBgAA0DnBDQAAoHPr2setkkwOfbZ9+3yfsKmh13LL\nF28flG++fjhkyrZT58duO7RpmD8P3jvsybV1y/BtHrpvvqfdwX3DPmzbTxo+97Rtw5HNdmyff90z\nzjlvMO+SpzxzUH7ys140KF90yd/JQqb7nk0n6s2LzJs2PRbbdBkAenLZZZctPLNWOFLowl3c/FoN\nbGj2YQAAAJ0T3IANqapOqqoPVNUfzbouAABrbV2bSm5Ksm2ifOmljz08ffXUbfk//ukbB+Uztp8+\nXNn98w0AD9x51/B1Ng/f1tyhYUPEzRPzt54yzK5n7NgxKG+f+oQe9tWPPjz91H/4ksG8xzz12wfl\nXTsXHmpgsdv9J8MmpSu1WBrXbJLjyMuTfCLJaUstCCzPZZc9dVCumh9Pp7W26HNrkSaN089cYePH\nFVnLdQPMkituwIZTVecn+bYkvzrrugAArAfBDdiI/mOSf51FLiJX1RVVtbeq9u7bt2/9agYAsAYE\nN2BDqapvT3JLa+2axZZrrV3ZWtvTWtuzc6rZMgDARrOufdxahj+Pb94633Z+bm77YNm5PGRQ/vL+\nA4Pyps3zmfOe/fcN520alrdMDRewY8d8ee7AcL1fnht+JA9/4p5B+Xkv/aHD0xc95TmDeZuXiMGT\nPe2mF13pLf7hBPbUJM+tqudk1G32tKr6zdbai5Z4HjDl8sunb8O/cA+xxfqwLUW/M4Bj54obsKG0\n1n68tXZ+a+3CJC9I8udCGwBwvBPcAAAAOreuTSUBVlNr7Z1J3jnjagAArLmZBrf999x5ePq2O4Z9\nze7fPOzzds/B4fzJodk2bzt1MO/g/jszXHjY5+2+ufkeYydP9R572CWXDsrP/J6XDsqP+8b5fm33\n5ujpswbA7Ol9BrBRaCoJAADQOcENAACgc4IbAABA59a1j1tlmBS3nzrfj23btm2DZQ8ePDgoHzp0\naLiyrVsPT27ePHwbc1unynPDHmUPmZj/qEecNZj37Oc/d1B+8rc+MwvZOlWeqqF+bADM3GWXzY/V\ndixjsQEwW664AQAAdE5wAwAA6Ny6Dwdw0gLTD7ZUQ8P5zDk3N2ykuG3rQwblg4eG6zr/grMPT3/3\n84dNIb/peVNNIzcPhxqYHFjgREi9J8J7BDieXH755bOuAgBrwHk5AABA5wQ3AACAzgluAAAAnVv3\nPm6Ttm47+fD0g3u0Dau2aSpiDocLmLr9/9Tazjlv96D8xMc/5vD0NzzpsYN5pz7iggXrmwxv+T/T\nD+8orWZSl/oBZu/yyy9beiEANjzn3gAAAJ0T3AAAADq3ZHCrqguq6h1V9fGq+lhVvXz8+BlV9faq\n+tT4/9PXvroAsDjHLY6F7Qfo1XK6aR1K8orW2vur6tQk11TV25O8JMmftdZeXVWvTPLKJD+2khef\nm8iNh+aGGXL/wWF5++apqs7N93E7dGg4b/uu7YPyabuGY7HtOGV+BLnTT9sxXO+mrYvWebJW0/3y\nplPwal7OXGpUu+VyiZXjQVXtSvKrSR6bpCX5J621d8+2VnRkzY5bfapZV+B4c4JtP8BGseR5fGvt\nptba+8fTdyb5RJLzkjwvyevHi70+yXeuVSUBpvynJH/SWnt0kq/NaL8ESRy3ODa2H6BXK7oAU1UX\nJnl8kvcmOau1dtN41s1JzlrgOVdU1d6q2nvrrbceQ1UBkqrameRpSV6bJK21g621O2ZbK3p1rMet\nffv2rUs96dOxbj/79+9fl3oCJ4ZlB7eq2pHkd5P8SGvtK5PzWmsto+ZKD9Jau7K1tqe1tmf37t1H\nWgRgJS5KcmuSX6+qD1TVr1bVKdMLOflmNY5bO3fuXIea0qPV2H62b99+pEUAjsqyhiKrqi0Z7bx+\nq7X2e+OHv1hV57TWbqqqc5Lcspx1Te7l7j144PD0gQMHBsvNzR3KYjZNZs6p/m+nnHLyoHzajmE/\ntrvuvPvw9L47hid0O+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pOoYeeAkKF3NSDUAhtgwathyY2pymkhFILPSlZqIipBIZcklAw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\nAACUE24AAADlhBsAAEC53wBVWX1qSi4/qQAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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er6geH088Y/s1/77D87Kr67ALLbVX101V1TZJrho89rqreX1Vfraqrq+qHR+Z/flVdOfz+uaWq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\n16nbsKE75u0Ll3fva3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2d2Fo7u7V2dpJPJnlha83NSgCAtcZxDxMjuLGsWmszSX4myfuS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\nOeEGAABQTrgBAACUE24AAADlfgNYoi5KTY4EZQAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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845Cfsujf58Htm48mbPe3qNeW+e7vxib36NRN3UtzRu8vi5l73xjN7d6jcOHFr3i92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\nADBwEjcAAICBW9Kukm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6XLtPrrjS2EyyuY+1v7Fh7vwvJtjt0ttXD2SaHxnaCOSxL4tZa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\n","text/plain":["<Figure size 1080x1080 with 6 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"mw9L6hbLZaOO","colab_type":"code","outputId":"af146761-7f94-4169-8d29-d2c7b2fcc799","executionInfo":{"status":"ok","timestamp":1563169087082,"user_tz":240,"elapsed":890,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["output_lr.shape\n","# a = output_lr.reshape([10,10])\n","# a = output_lr.reshape([10,10])\n","# output_lr.cpu().detach().numpy().squeeze()\n","interpol_img = cv2.resize(output_lr.cpu().detach().numpy().squeeze(), (32,32))\n","interpol_img.shape\n","aa = output_lr.cpu().detach().numpy().squeeze()\n","aa = aa.transpose(1,2,0)\n","aa.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10, 10, 3)"]},"metadata":{"tags":[]},"execution_count":159}]},{"cell_type":"code","metadata":{"id":"oSjiojuCDCEa","colab_type":"code","outputId":"5d093267-53e8-4b4a-bd90-3681d0d9764d","executionInfo":{"status":"ok","timestamp":1563168949538,"user_tz":240,"elapsed":835,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# resize_tensor(output_lr.cpu(), 28, 28)\n","output_lr.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 3, 10, 10])"]},"metadata":{"tags":[]},"execution_count":148}]},{"cell_type":"code","metadata":{"id":"aLYuqgiHDnCl","colab_type":"code","colab":{}},"source":["import cv2"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"m6P0eosAGGop","colab_type":"code","colab":{}},"source":["aa = cv2.resize(output_lr.cpu().detach().numpy().squeeze(), (28,28))\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pkPoxZukGI9u","colab_type":"code","outputId":"db77d633-b3ad-42a3-828c-f90b1902e9d8","executionInfo":{"status":"ok","timestamp":1563151784119,"user_tz":240,"elapsed":824,"user":{"displayName":"Kimia Tajik","photoUrl":"https://lh4.googleusercontent.com/-mUJ8nuTtolU/AAAAAAAAAAI/AAAAAAAAACk/0ZUcpbTE5rY/s64/photo.jpg","userId":"03033357861750849244"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["aa.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(28, 28)"]},"metadata":{"tags":[]},"execution_count":219}]},{"cell_type":"code","metadata":{"id":"N0rwsFWRGwWw","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}